Run Athena using the metadata CLI
| Feature | Status |
|---|---|
| Stage | PROD |
| Metadata | |
| Query Usage | |
| Data Profiler | |
| Data Quality | |
| Lineage | |
| DBT | |
| Supported Versions | -- |
| Feature | Status |
|---|---|
| Lineage | |
| Table-level | |
| Column-level |
In this section, we provide guides and references to use the Athena connector.
Configure and schedule Athena metadata and profiler workflows from the OpenMetadata UI:
Requirements
OpenMetadata 0.12 or laterTo deploy OpenMetadata, check the Deployment guides.
To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.
The Athena connector ingests metadata through JDBC connections.
According to AWS's official documentation:
If you are using the JDBC or ODBC driver, ensure that the IAM permissions policy includes all of the actions listed in AWS managed policy: AWSQuicksightAthenaAccess.
This policy groups the following permissions:
athena– Allows the principal to run queries on Athena resources.glue– Allows principals access to AWS Glue databases, tables, and partitions. This is required so that the principal can use the AWS Glue Data Catalog with Athena.s3– Allows the principal to write and read query results from Amazon S3.lakeformation– Allows principals to request temporary credentials to access data in a data lake location that is registered with Lake Formation.
And is defined as:
You can find further information on the Athena connector in the docs.
Python Requirements
To run the Athena ingestion, you will need to install:
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Athena.
In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following JSON Schema
1. Define the YAML Config
This is a sample config for Athena:
Source Configuration - Service Connection
- awsAccessKeyId & awsSecretAccessKey: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and authorize your requests (docs).
Access keys consist of two parts: An access key ID (for example, AKIAIOSFODNN7EXAMPLE), and a secret access key (for example, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY).
You must use both the access key ID and secret access key together to authenticate your requests.
You can find further information on how to manage your access keys here.
awsSessionToken: If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID and AWS Secrets Access Key. Also, these will include an AWS Session Token.
awsRegion: Each AWS Region is a separate geographic area in which AWS clusters data centers (docs).
As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.
Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.
You can find further information about configuring your credentials here.
endPointURL: To connect programmatically to an AWS service, you use an endpoint. An endpoint is the URL of the entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.
Find more information on AWS service endpoints.
profileName: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command. When you specify a profile to run a command, the settings and credentials are used to run that command. Multiple named profiles can be stored in the config and credentials files.
You can inform this field if you'd like to use a profile other than default.
Find here more information about Named profiles for the AWS CLI.
assumeRoleArn: Typically, you use AssumeRole within your account or for cross-account access. In this field you'll set the ARN (Amazon Resource Name) of the policy of the other account.
A user who wants to access a role in a different account must also have permissions that are delegated from the account administrator. The administrator must attach a policy that allows the user to call AssumeRole for the ARN of the role in the other account.
This is a required field if you'd like to AssumeRole.
Find more information on AssumeRole.
assumeRoleSessionName: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role is assumed by different principals or for different reasons.
By default, we'll use the name OpenMetadataSession.
Find more information about the Role Session Name.
assumeRoleSourceIdentity: The source identity specified by the principal that is calling the AssumeRole operation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role.
Find more information about Source Identity.
s3StagingDir: The S3 staging directory is an optional parameter. Enter a staging directory to override the default staging directory for AWS Athena.
workgroup: The Athena workgroup is an optional parameter. If you wish to have your Athena connection related to an existing AWS workgroup add your workgroup name here.
Source Configuration - Source Config
The sourceConfig is defined here:
markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
includeTables: true or false, to ingest table data. Default is true.
includeViews: true or false, to ingest views definitions.
databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here
Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.
Workflow Configuration
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
Advanced Configuration
Connection Options (Optional): Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
- You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.
Query Usage
The Query Usage workflow will be using the query-parser processor.
After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.
1. Define the YAML Config
This is a sample config for BigQuery Usage:
Source Configuration - Source Config
You can find all the definitions and types for the sourceConfig here.
queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.
stageFileLocation: Temporary file name to store the query logs before processing. Absolute file path required.
resultLimit: Configuration to set the limit for query logs
queryLogFilePath: Configuration to set the file path for query logs
2. Run with the CLI
After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
Data Profiler
The Data Profiler workflow will be using the orm-profiler processor.
After running a Metadata Ingestion workflow, we can run Data Profiler workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.
1. Define the YAML Config
This is a sample config for the profiler:
Source Configuration - Source Config
You can find all the definitions and types for the sourceConfig here.
generateSampleData: Option to turn on/off generating sample data.
profileSample: Percentage of data or no. of rows we want to execute the profiler and tests on.
threadCount: Number of threads to use during metric computations.
processPiiSensitive: Optional configuration to automatically tag columns that might contain sensitive information.
confidence: Set the Confidence value for which you want the column to be marked
timeoutSeconds: Profiler Timeout in Seconds
databaseFilterPattern: Regex to only fetch databases that matches the pattern.
schemaFilterPattern: Regex to only fetch tables or databases that matches the pattern.
tableFilterPattern: Regex to only fetch tables or databases that matches the pattern.
Processor Configuration
Choose the orm-profiler. Its config can also be updated to define tests from the YAML itself instead of the UI:
tableConfig: tableConfig allows you to set up some configuration at the table level.
- You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from here
2. Run with the CLI
After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
Note now instead of running ingest, we are using the profile command to select the Profiler workflow.
Lineage
You can learn more about how to ingest lineage here.